HAPB-FL: A Federated Learning and Hierarchical Key Agreement Framework with Adaptive Privacy Budgeting for Privacy Preservation in IoT - Liu, Juan; Zhang, Yujing (Informatica, 2025)
Aiming at the problems of low efficiency, insufficient security and module fragmentation in the existing privacy protection schemes for the Internet of Things, a new privacy protection model is proposed. By introducing a data sensitivity weighting strategy through adaptive privacy budget allocation, the privacy budget and noise scale are dynamically adjusted. The cross-layer synchronization of root keys through hierarchical logical key tree subgroups and a decentralized architecture adapts to the low computing power requirements of edge devices. These two components simultaneously form a closed-loop synergy with federated learning. The experimental results show that the accuracy of the optimized federated algorithm gradually stabilizes after 100 rounds of communication and reaches 92.5±1.5%. The maximum recognition accuracy, predicted recall rate, and the harmonic mean of the recall rate reach 96.35%, 95.88%, and 96.10% respectively. The evaluation of the fusion model revealed that the overall coincidence degree of the output data of this model with the original data trajectory reached 97.6%. The average training time and the processing time of a single piece of data reached 1.2±0.2 min and 2.3±0.2 ms, respectively. The resource occupation ratio of this model reached a maximum of 51.3%. The above results indicate that the privacy protection model proposed by the research institute meets the requirements of large-scale privacy data processing and performs well in terms of real-time performance and efficiency.